RBI Working Paper Series No. 03
Real Time Business Conditions Index :
A Statistically Optimal Framework for India
*The state of the real economy evolves in a continuous fashion. Economic agents and policy makers, while making decisions in real time, require accurate and timely understanding of the state of real activity. In the light of the changing nature of the economy where increasingly more and more activities are being channelised through both organised and unorganized business sectors, the measurement of business condition on real time basis is of paramount importance. In this context, in order to achieve an accurate and timely estimate of the state of real activity in a systematic, replicable and statistically optimal manner, this paper proposes a framework to construct a real-time business conditions index for India. The study is primarily motivated by the seminal work of Aruoba, Diebold and Scotti (2009), for the high frequency business conditions assessment for the U.S. economy. Based on various economic indicators measured at different frequencies, this paper develops a real-time business conditions index for India following a dynamic factor model framework for extracting signals from continuously evolving states. A Kalman filter routine is used for signal extraction from state-space representation as well as evaluation of likelihood function. Empirical results show that this coincident indicator tracks the overall economic activity reasonably well.
- JEL Classification: C61, E32, E37
- Key Words: Business cycle; Dynamic factor model; Turning points; State-space model; Expansion
The state of the real economy of a country evolves in a continuous fashion. Economic agents and policy makers, making decisions, in real time, require accurate and timely estimates of the state of real activity. In the light of the changing nature of the economy, where more and more activities are being channelised through both organized and unorganized business sectors, the assessment of business condition on real time basis is of paramount importance, particularly for central banks. From mid-1980s until 1998, the Reserve Bank of India (RBI) used a monetary-targeting framework. In the year 1998, the RBI’s Working Group on Money Supply, in its report, pointed out that monetary policy exclusively based on money demand could lack precision and hence, it was necessary to monitor a set of additional indicators for monetary policy formulation. Accordingly, the RBI adopted a multiple indicator approach from 1998 wherein, besides monetary aggregates, information pertaining to currency, credit, fiscal position, merchandise trade, capital flows, inflation rate, exchange rate, refinancing and transactions in foreign exchange etc., were juxtaposed with data on output and the real sector activity for drawing policy perspectives. The widening range of variables monitored and studying their dynamic interactions are now possible partly because of the development of more sophisticated econometric models. In this context, in 2002, the RBI's Working Group of Economic Indicators provided importance to deal with the business cycle analysis and to construct a composite index of leading indicators of Indian economy. In 2007, the RBI's Working Group of Leading Indicators for Indian Economy, in its report, recommended two series, viz., monthly Index of Industrial Production (IIP) and quarterly Non-Agricultural GDP, as the reference frame of business cycle in India. The Group also constructed Composite Index of Leading Indicators (CILI) for each of these two reference series following international best practices. As proposed by the Group, the outlook for business cycle movement for 2-3 quarters ahead is regularly examined internally in RBI and serves as an important input to the monetary policy making.
It has been, however, observed that the proposal of the Working Group of Leading Indicators to provide an outlook for business condition of the Indian economy is not sufficient on real time basis due to the following reasons. Firstly, most frequent data used for developing leading indicators is observed on monthly basis. For real time measurement, moving beyond the monthly frequency is a basic pre-requisite. Some important indicators (e.g., asset prices, yield curve term premium) are observed at daily frequency which potentially contains important information on the overall economic activity. Secondly, the report did not take into account the assumption of continuously evolving state of the economy, which is essential to real time measurement. Lastly, the provisional and partially revised data used for the leading index also affects the performance to predict future movements in aggregate economic activity in the real-time framework (Diebold and Rudebusch, 1991).
Against this backdrop, we propose a framework motivated by the earlier work of Aruoba, Diebold and Scotti (2009), for the high frequency business conditions assessment for India in a systematic, replicable and statistically optimal manner. Giving the latest information of various macroeconomic indicators of different frequencies, our objective is to assess the current state of economic activity based on a real-time index and to update our assessment as more information flows in. Our assessment is as on today, and not beyond it. In that sense, the index is coincident (not leading) to the business condition.
The paper is organised as follows: Section 2 reviews the literature on the real-time data analysis. Section 3 describes the empirical analysis concerned with the development of real-time business conditions index for Indian economy. The description of software used for empirical analysis is mentioned in Section 4. Finally, Section 5 summarises the results, with a few concluding remarks.
2. Literature Review
In empirical econometrics, the use of real-time data is not a recent area of study. A long literature can be mentioned in this regard. Early studies of real-time data focused on the sensitivity of certain statistics to data vintage. Gartaganis and Goldberger (1955) did the first work on real-time data analysis. They mainly confined themselves to the properties of statistical discrepancy between Gross National Product (GNP) and gross national income in United States, after data were revised in 1954. Howrey (1978) focused on the use of preliminary data in econometric forecasting and indicated clearly that the intelligent use of preliminary data would be expected to result in a meaningful reduction in prediction error variances. Diebold and Rudebusch (1991) examined the ability of composite index of leading economic indicators to predict future movements in aggregate economic activities based on real-time analysis. They used the provisional and partially revised data for the leading index that were actually available historically, along with recursive out-of-sample forecasts. They found substantial deterioration of forecasting performance in the real-time framework. Orphanides and Simon van Norden (2002) examined the reliability of several detrending methods for estimating the output-gap in real time. They focused on the extent to which output-gap estimates were updated over time as more information arrived and data were revised. They suggested that, great caution would be required for measuring output-gap on real-time basis.
Later research posed the problem more formally as a signal-extraction problem. Evans (2005) focused on estimating high-frequency GDP, equated business conditions with GDP growth and used state-space methods to estimate daily GDP growth using data on preliminary, advanced, and final releases of GDP and other macroeconomic variables. Anderson and Gascon (2009) used a state-space model to estimate the “true” unobserved measure of total output in the U.S. economy. The analysis used the entire history (i.e., all vintages) of selected real-time data series to compute revisions and corresponding statistics for those series. The revision statistics, along with the most recent data vintage, were used in a state-space model to extract filtered estimates of the “true” series.
This study is primarily motivated by an empirical study of Aruoba, Diebold and Scotti (2009) on the U.S. economy. They constructed a framework for measuring economic activity at high frequency, potentially in real time. They used a variety of stock and flow data observed at mixed frequencies and performed a prototype empirical application for illustrating the gains achieved by moving beyond the customary monthly data frequency. The four key ingredients of their work are as follows:
1. Treatment of business conditions as an unobserved variables, related to the observed indicators. Latency of business conditions is consistent with economic theory (e.g., Lucas 1977), which emphasizes that the business cycle is not about any single variable, but the dynamics and interactions (or comovements) of many variables.
2. Explicit incorporation of business conditions indicators measured at different frequencies. Important business conditions indicators arrive at a variety of frequencies, including quarterly (e.g., GDP), monthly (e.g., industrial production), weekly (e.g., employment), and continuously (e.g., asset prices), and the incorporation of all of them provides continuously updated measurements.
3. Explicit incorporation of indicators measured at high frequencies. As the goal is to track the high frequency evolution of real activity, it is important to incorporate (or at least not exclude from the outset) the high frequency information flow associated with high frequency indicators.
4. Extraction and forecasting of latent business conditions using linear yet statistically optimal procedures, which involve no approximation. The appeal of exact as opposed to approximate procedures is obvious, but achieving exact optimality is not trivial, due to complications arising from temporal aggregation of stocks versus flows in systems with mixed-frequency data.
They proposed a dynamic factor model that permitted exactly optimal extraction of the latent state of macroeconomic activity being illustrated by a four-variable empirical application with a daily frequency, and in a parallel calibrated simulation (detailed theory mentioned in the technical appendix). The following four indicators with varying frequencies were chosen as business conditions indicators:
- Yield curve term premium, defined as the difference between 10-years and 3-months U.S. Treasury yield, at daily frequency.
- Initial claims for unemployment insurance, a weekly flow variable.
- Employees on non-agricultural payrolls, a monthly stock variable.
- Real GDP, a quarterly flow variable.
The real activity indicator thus obtained from the empirical analysis threw new lights on the area of business cycle measurement and simultaneously, outperformed the so-called National Bureau of Economic Research (NBER) chronology in some economic as well as statistical sense. First, although the real activity indicator broadly cohered with the NBER chronology, it had a propensity to indicate earlier turning points, especially peaks. Second, the indicator was available at high frequency and hence, a useful “nowcast”, whereas the NBER chronology was available only monthly and with verylong lags. Third, it was evident that, incorporation of weekly data in real activity indicator was very helpful for providing real time information, as compared to NBER chronology. However, incorporation of daily data did not improve the performance of the indicator; still a daily state-space setup was needed to accommodate the variation in weeks per month and weeks per quarter. Fourth, based on a simulation calibrated to the empirical results, it was observed that, incorporating high frequency data improved the accuracy of the extracted factor. Lastly, the real time performance (preferably, daily) of the business conditions would be assessed at any point of time by re-estimating the system based on latest-vintage data.
Presently, six macroeconomic indicators are used to construct the Aruoba-Diebold-Scotti Business Conditions Index (ADS Index). These are weekly initial jobless claims, quarterly real GDP, monthly payroll employment, monthly industrial production, monthly real personal income less transfers, and monthly real manufacturing and trade sales. All these are important and widely monitored. The ADS Index is updated weekly, following the release of that week’s new and/or revised component indicator data.
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